AI Visibility Readiness Framework · public trust surface

Every AVR Score must be reconstructable from receipts.

citability.dev runs the AI Visibility Readiness Framework on your domain. The Framework (acronym AVR) measures three components, Visibility, Recommendability, and Citability (VRC), and rolls them into a single AVR Score (0-100). Every score drills down to the raw model run, version, prompt, extracted citations, classification logic, and hash.

01 /VRC components

VVisibility

Whether the brand appears at all for the query and model.

RRecommendability

Whether the answer frames the brand as a viable recommendation.

CCitability

Whether the answer links or cites the brand/domain as a source.

02 /AVR Score

AVR Score = weighted(V, R, C)

V = visibility across query × model receipts
R = recommendation quality and rank position
C = citation/link extraction quality

band: ≥70 healthy · 40-69 at-risk · <40 invisible

03 /Proof: valid audits

Every score ships with a receipt.

A receipt is immutable, timestamped, model-specific, query-specific, hash-addressed, and suitable for export, appeal, and public verification.

Open sample public receipt

GET /v1/receipts/rcpt_01HXK9F2

{
  "id": "rcpt_01HXK9F2",
  "query": "best b2b saas payment processor",
  "verdict": "mixed",
  "avr": 62,
  "models": ["chatgpt/gpt-5.1", "claude/4.5", "perplexity/sonar", "gemini/2.5"],
  "signature": "sha256:a14f...b902"
}

04 /Data sources

OpenAI

ChatGPT

gpt-5.1

Anthropic

Claude

claude-4.5-sonnet

Perplexity

sonar-pro

Google

Gemini

gemini-2.5-pro

05 /Observable pipeline

Normalize project, domain, models, query set, and schedule.

Dispatch prompt jobs across pinned model versions.

Persist raw responses, timing, sources, and provider metadata.

Extract linked citations, in-text mentions, and referenced domains.

Classify cited, mentioned, competitor-mentioned, or absent.

Calculate V/R/C components and AVR rollup.

Sign receipts and expose public verification when enabled.

06 /Methodology cluster · published research

The methodology behind the score.

The AVR Framework + receipt format are documented on this page. The ai-visibility-tools-methodology cluster on the blog walks through the category-level methodology any AI visibility tool buyer or vendor should adopt: calibration receipts, three-anchor calibration against a DR-citation curve, and V/R/C separation as independent axes.

pillar
The 0% ChatGPT Citation Trap
Why most AI visibility tools mismeasure ChatGPT, the technical fix, and the calibration receipt format every audit should ship.
spokescheduled 2026-05-11
Three-Anchor Calibration Methodology
spokescheduled 2026-05-18
Visibility != Recommendability != Citability

07 /Agent Readiness · parallel module

Agent Readiness sits parallel to the AVR Framework.

The AI Visibility Readiness Framework measures whether AI systems can find, recommend, and cite you today. Agent Readiness measures whether your site is prepared for the next layer: agents that act on web content via WebMCP, MCP, Stripe Link wallets, and the Agentic Commerce Protocol. In development as a separate module, not a sub-component of AVR.